Previous work [1]}, [2]}, [3]} attempted to directly employ network pruning methods to compress the generator but obtain unimpressive results, as shown in Figure REF .
The similar phenomenon also occurred in SAGAN shown in Appendix A.
A potential reason is that these methods failed to take into account the generator and the discriminator must follow the Nash equilibrium state to avoid the mode collapse in the adversarial learning.
In other words, these methods simply compress the generator while maintaining the original capability of the discriminator, which resulted in breaking the balance in adversarial learning.
As shown in Figure REF  and  REF , when compressing the generator, the loss of the discriminator gradually tends to zero, such a situation indicates that the capacity of the discriminator significantly surpasses that of the lightweight generator. Furthermore, the capacity imbalance between the generator and discriminator leads to the mode collapse problem [4]}.
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